Coincidently, Disney’s “Sleeping Beauty” was released on the same day 29 years later. So gather your favorite Disney Princess, switch on the “The Computer Wore Tennis Shoes” and enjoy the latest in AIOps, ITOps, and IT infrastructure monitoring.
There’s a dumb reason why your AI project will fail.
According to the Harvard Business Review, one critical factor is often left out when we discuss artificial intelligence: How do you actually integrate artificial intelligence into your business?
The answer is AIOps. At its most basic, AIOps boils down to having not just the right hardware and software but also the right team: developers and engineers with the skills and knowledge to integrate AI into existing company processes and systems.
A good AIOps team will help a business build a robust production environment that is dependable, flexible, and scalable. This team can either be in-house, which will cost more upfront, but give businesses more control, or contracted out, which will lower overheard, as well as administrative costs, but will likely mean giving up the option of running a proprietary system, as well as some control.
But, if you’re considering integrating AI into your business, AIOps can help you avoid costly failures and keep the system running smoothly.
There are 8 misleading myths about artificial intelligence.
This article in InfoWorld puts forth some common myths and helps explain how some fears are just not so worrisome. The following key points are:
- 1. Myth: AI is going to take away jobs.
Reality: Mostly AI will change existing jobs and create new ones.
- 2. Myth: AI is smarter than people.
Reality: AI is as smart as you program it.
- 3. Myth: AIOps is predominantly based on event management and correlation.
Reality: Maybe at first but it is evolving.
- 4. Myth: Companies don’t need an AI strategy.
Reality: Oh yes you do!
- 5. Myth: AI will make medical decisions and diagnoses.
Reality: Yes, but AI won’t have the last word.
- 6. Myth: I have no idea what the AI is doing and if I can trust it.
Reality: AI is much more transparent now.
- 7. Myth: I need a data lake to train my AI.
Reality: Drain the swamp. Get rid of that unstructured data.
- 8. Myth: Modeling determines outcome.
Reality: You can’t be certain of that.
You should have 5 AIOps skills to add to your DevOps resume.
According to this article in TechTarget, job seekers are given some essential items related to AIOps to learn and add to your skillset in order to help you become a more valuable candidate for positions in IT, DevOps, or SecOps.
- 1. Monitoring Centralization and Interpretation
Machine learning and AI work best with unified data sets. AIOps professionals must harmoniously integrate data ingestion — monitoring — tools with data analysis tools, which often means connecting open source tools to closed source tools.
- 2. Systems Design and Advocacy
AIOps is predicated on solving puzzles, which requires proper tooling. Professionals dive deep into their infrastructure to learn its strengths, weaknesses, and overall composition. They must then design AIOps systems based on systemic priorities and/or pain points.
- 3. Data Analysis and Management
Exceptional data scientists are costly and hard to find. When an organization can’t afford to hire enough, AIOps professionals are often left to handle many of the tasks that would otherwise fall to those individuals.
- 4. Security and Event Recognition
Proper configuration is key to realizing the fullest potential of AIOps tools. For example, setting threshold or event-based alerts is crucial to infrastructure oversight. And while these platforms are adept at catching nefarious actors or notable problems, it’s up to the IT team to respond.
- 5. Cross-Functional Teamwork
AIOps touches many facets of an organization and brings together people from multiple departments. AIOps professionals must understand micro- and macro-level priorities, work with different personalities, and balance actions with goals.
MLOps and AIOps are not the same thing.
According to this article in Rackspace, AIOps and MLOps options are often being explored by tech leaders who are looking to automation to improve their businesses. While they share some similarities, these are two different entities utilizing different technologies and disciplines.
AIOps is about increased efficiency in IT operations, achieved by automating incident/management diagnostics and intelligently finding the root cause through machine learning. By sifting through the noise generated by monitoring systems and reducing the false positives, these solutions present technical teams with high-quality information that is easy to understand, so that they can get to work on a resolution.
MLOps, on the other hand, focuses on creating an automated pipeline for bringing machine learning models into production. It looks to overcome the disconnect between data science or data ops teams and infrastructure teams, to get models into production faster and more often. Importantly – and in contrast to AIOps – MLOps doesn’t directly refer to a machine learning capability per se, with algorithms processing data. Rather, it’s a way to manage and streamline the building, deployment, and maintenance of those algorithms.
Just getting started with AIOps and want to learn more? Read the eBook, “Your Guide to Getting Started with AIOps”>